Choose metric

Extracts metrics from metric_data from fairness object. It allows to visualize and compare parity loss of chosen metric values across all models.

choose_metric(x, fairness_metric = "FPR")

Arguments

x

object of class fairness_object

fairness_metric

char, single name of metric, one of metrics: TPR - parity loss of True Positive Rate (Sensitivity, Recall, Equal Odds) TNR - parity loss of True Negative Rate (Specificity) PPV - parity loss of Positive Predictive Value (Precision) NPV - parity loss of Negative Predictive Value FNR - parity loss of False Negative Rate FPR - parity loss of False Positive Rate FDR - parity loss of False Discovery Rate FOR - parity loss of False Omission Rate TS - parity loss of Threat Score ACC - parity loss of Accuracy STP - parity loss of Statistical Parity F1 - parity loss of F1 Score

Value

chosen_metric object It is a list with following fields:

• parity_loss_metric_data data.frame with columns: parity_loss_metric and label

• metric chosen metric

• label character, vector of model labels

Examples

data("german")

y_numeric <- as.numeric(german$Risk) - 1

lm_model <- glm(Risk ~ .,
  data = german,
  family = binomial(link = "logit")
)

explainer_lm <- DALEX::explain(lm_model, data = german[, -1], y = y_numeric)
#> Preparation of a new explainer is initiated
#>   -> model label       :  lm  (  default  )
#>   -> data              :  1000  rows  9  cols 
#>   -> target variable   :  1000  values 
#>   -> predict function  :  yhat.glm  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package stats , ver. 4.1.1 , task classification (  default  ) 
#>   -> predicted values  :  numerical, min =  0.1369187 , mean =  0.7 , max =  0.9832426  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -0.9572803 , mean =  1.940006e-17 , max =  0.8283475  
#>   A new explainer has been created!  

fobject <- fairness_check(explainer_lm,
  protected = german$Sex,
  privileged = "male"
)
#> Creating fairness classification object
#> -> Privileged subgroup		: character ( Ok  )
#> -> Protected variable		: factor ( Ok  ) 
#> -> Cutoff values for explainers	: 0.5 ( for all subgroups ) 
#> -> Fairness objects		: 0 objects 
#> -> Checking explainers		: 1 in total (  compatible  )
#> -> Metric calculation		: 13/13 metrics calculated for all models
#>  Fairness object created succesfully  


cm <- choose_metric(fobject, "TPR")
plot(cm)

# \donttest{
rf_model <- ranger::ranger(Risk ~ .,
  data = german,
  probability = TRUE,
  num.trees = 200
)


explainer_rf <- DALEX::explain(rf_model, data = german[, -1], y = y_numeric)
#> Preparation of a new explainer is initiated
#>   -> model label       :  ranger  (  default  )
#>   -> data              :  1000  rows  9  cols 
#>   -> target variable   :  1000  values 
#>   -> predict function  :  yhat.ranger  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package ranger , ver. 0.13.1 , task classification (  default  ) 
#>   -> predicted values  :  numerical, min =  0.07711508 , mean =  0.6957104 , max =  0.9973333  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -0.7042262 , mean =  0.004289608 , max =  0.6651608  
#>   A new explainer has been created!  

fobject <- fairness_check(explainer_rf, fobject)
#> Creating fairness classification object
#> -> Privileged subgroup		: character ( from first fairness object  ) 
#> -> Protected variable		: factor ( from first fairness object  ) 
#> -> Cutoff values for explainers	: 0.5 ( for all subgroups ) 
#> -> Fairness objects		: 1 object (  compatible  )
#> -> Checking explainers		: 2 in total (  compatible  )
#> -> Metric calculation		: 10/13 metrics calculated for all models ( 3 NA created )
#>  Fairness object created succesfully  

cm <- choose_metric(fobject, "TPR")
plot(cm)

# }